Luận án: Ảnh hưởng của mưa đến trượt lở đất và bản đồ tai biến huyện Khánh Vĩnh, Khánh Hòa

+È1+9Ƭ1+7ӌNH KHÁNH HÒA /8Ұ1È17,ӂ16Ƭ.Ӻ7+8Ұ7 73+ӖCHÍ 0,1+1Ă09 ĈҤ,+Ӑ&48Ӕ&*,$73 HCM 75ѬӠ1*ĈҤ,+Ӑ&%È&+KHOA 1*8<ӈ17+$1+'$1+ 1*+,Ç1&Ӭ8Ҧ1++ѬӢ1* &Ӫ$0Ѭ$ĈӔ,9Ӟ,75ѬӦ7/Ӣ 9¬;Æ<'Ӵ1*%Ҧ1ĈӖ7$,%,ӂ175ѬӦ7/Ӣ +8<ӊ1.+È1+9Ƭ1+7ӌNH KHÁNH HÒA Ngành: .ӻWKXұWÿӏDFKҩW Mã Vӕngành: 62.01 3KҧQELӋQÿӝFOұS 3*676+RjQJ7Kӏ7KDQK7Kӫ\ 3KҧQELӋQÿӝFOұS 3*6767ҥĈӭF7KӏQK 3KҧQELӋQ 3*6769NJ&Kt+LӃX 3KҧQELӋQ 3*6763KҥP4Xê1KkQ 3KҧQELӋQ 3*6767UҫQ9ăQ;XkQ 1*ѬӠ, +ѬӞ1*'Ү1. 767ҥ4XӕF'NJQJ /Ӡ,&$0Ĉ2$1 7iFJLҧ[LQFDPÿRDQÿk\OjF{QJWUuQKQJKLrQFӭXFӫDEҧQWKkQWiFJLҧ&iFNӃWTXҧ QJKLrQFӭXYjFiFNӃWOXұQWURQJOXұQiQQj\OjWUXQJWKӵFYjNK{QJVDRFKpSWӯEҩWNǤ PӝWQJXӗn nào, GѭӟLEҩWNǤKuQKWKӭFQjR9LӋFWKDPNKҧRFiFQJXӗQWjLOLӋX QӃXFy  ÿmÿѭӧFWKӵFKLӋQWUtFKGүQYjJKLQJXӗQWjLOLӋXWKDPNKҧRÿ~QJTX\ÿӏQK 7iFJLҧOXұQiQ 1JX\ӉQ7KDQK'DQK i 7Ï07Ҳ7/8Ұ1È1 Nghiên cӭu này trình bày kӃt quҧ nghiên cӭXWUѭӧt lӣ ÿҩWGRPѭDvà FiFSKѭѫQJSKiS thành lұp bҧQÿӗ dӵ báo tai biӃQWUѭӧt lӣ. &ѫVӣ nghiên cӭu là các lý thuyӃt vӅ quá trình thҩPQѭӟc hai chiӅu và tiêu chuҭn phá hӫy Mohr-Coulomb mӣ rӝQJWURQJP{LWUѭӡQJÿҩWNK{QJEmRKzDÿѭӧc sӱ dөQJÿӇ ÿiQK giá ҧQKKѭӣng cӫDPѭDÿӕi vӟi әQÿӏnh mái dӕF0ѭDÿѭӧF[HPQKѭ\Ӄu tӕ chính kích hoҥt làm phá hӫy các mái dӕFWUѭӧt tiӅPQăQJ1ѭӟFPѭDQJҩm vào mái dӕFOjPÿҩt ҭPѭӟWWăQJiSOӵFQѭӟc lӛ rӛng âm, giҧPÿӝ hút dính và sӭc chӕng cҳt trên mһt phá hӫy. Vì vұy, mái dӕc giҧm tính әQÿӏnh có thӇ dүQÿӃn phá hӫy.

Ngoài ra, các yӃu tӕ vӅ cҩu tҥo hình hӑc mái dӕc, mӵFQѭӟc ngҫPEDQÿҫXFNJQJÿѭӧc xem là các yӃu tӕ góp phҫQÿiQJNӇ ÿӕi vӟi sӵ mҩt әQÿӏnh cӫa mái dӕc. Mӝt mô hình sӕ JKpSÿ{LSKkQWtFKWKҩm-әQÿӏQKÿѭӧc sӱ dөQJÿӇ mô phӓng thҩm và әQÿӏnh mái dӕFGѭӟLFiFÿLӅu kiӋQP{LWUѭӡQJÿһFWUѭQJQKѭWtQKWKҩPFѭӡQJÿӝ PѭD và cҩu tҥo hình hӑc mái dӕc cho vùng nghiên cӭX3KѭѫQJSKiSSKҫn tӱ hӳu hҥQÿѭӧc sӱ dөng trong phân tích thҩm, kӃt quҧ cӫa sӵ WKD\ÿәi áp lӵFQѭӟc lӛ rӛQJkPÿѭӧc sӱ dөng phân tích әQÿӏnh mái dӕc bҵQJSKѭѫQJSKiSFkQEҵng giӟi hҥn Morgenster-Price. Nhӳng mӕi quan hӋ giӳa hӋ sӕ an toàn mái dӕc YjFѭӡQJÿӝ PѭDKӋ sӕ thҩm, góc dӕc, chiӅu cao mái dӕFÿѭӧc rút ra. KӃt quҧ phân tích cho thҩy hӋ sӕ thҩm có vai trò quan trӑQJÿӕi vӟi sӵ әQÿӏnh cӫa mái dӕc.

Khi thӡLJLDQPѭDNpRGjLKѫQQJj\KӋ sӕ DQWRjQWKD\ÿәi nhiӅu nӃu mái dӕc cҩu tҥo bӣLÿҩt có hӋ sӕ thҩm nhӓ YjtWWKD\ÿәLKѫQNKLPiLGӕc cҩu tҥo bӣLÿҩt có hӋ sӕ thҩm lӟn. HӋ sӕ DQWRjQWKD\ÿәi theo thӡi gian GѭӟLFiFFѭӡQJÿӝ Pѭa khác nhau, hӋ sӕ an toàn càng giҧPNKLFѭӡQJÿӝ PѭDFjQJOӟn. 1Jѭӥng FѭӡQJÿӝ PѭDmái dӕc phá hӫy phө thuӝc thӡLJLDQPѭD. CѭӡQJÿӝ PѭDÿҥt 10 mm/h, sau thӡLJLDQKѫQQăP ngày mái dӕc mҩt әQÿӏnh.

Ӭng dөQJFiFSKѭѫQJSKiS[iFVXҩt thӕng kê gӗm tӹ sӕ tҫn suҩt (FR), chӍ sӕ thӕng kê (SI), trӑng sӕ chӭng cӭ (WoE) và hӗi quy logistic (LR) tích hӧp vӟi GIS (Phҫn mӅm Ilwis mã nguӗn mӣ ÿӇ thành lұp các bҧQÿӗ dӵ báo tai biӃQWUѭӧt lӣ ÿҩt huyӋn Khánh 9ƭQKWӍnh Khánh Hòa. ii &iFÿLӇPWUѭӧt lӣ ÿm[ҧy UDÿѭӧc thu thұp và lұp thành bҧQÿӗ phân bӕ không gian các ÿLӇPWUѭӧt lӣ ÿLӇPWUѭӧt lӣ). Các yӃu tӕ ҧQKKѭӣng ÿӃQWUѭӧt lӣ gӗm (11 yӃu tӕ): FDRÿӝ, góc dӕFKѭӟng dӕc, chӍ sӕ ҭPѭӟWÿӏDKuQKÿӝ uӕn cong bӅ mһt mái dӕc, thҥch hӑc, khoҧQJFiFKÿӃQÿѭӡng giao thông, khoҧQJFiFKÿӃn sông suӕi, khoҧQJFiFKÿӃn ÿӭt gãy, chӍ sӕ thӵc vұWYjOѭӧQJPѭDOӟn nhҩWQăP%ҧQÿӗ trӑng sӕ các yӃu tӕ ҧnh KѭӣQJÿѭӧc thành lұp dӵa vào mӕi liên quan không gian giӳa các yӃu tӕ ҧQKKѭӣng và phân bӕ FiFÿLӇPWUѭӧt lӣ theo ciFSKѭѫQJSKiS)56,:R(Yj/5 BҧQÿӗ dӵ báo tai biӃQWUѭӧt lӣ ÿѭӧc thành lұp bҵng viӋc chӗng lӟp các bҧQÿӗ trӑng sӕ các yӃu tӕ ҧQKKѭӣQJYjÿѭӧc chuҭQKyDÿӇ có phân bӕ chuҭn hoһc gҫn phân bӕ chuҭn. Áp dөQJSKѭѫQJSKiSÿӝ lӋch chuҭn (Standard Deviation Classification) chia bҧQÿӗ chӍ sӕ tai biӃQWUѭӧt lӣ làm 5 phân vùng vӟi các mӭFÿӝ tai biӃQWUѭӧt lӣ khác nhau: rҩt thҩp, thҩp, trung bình, cao và rҩt cao.

Sӱ dөQJÿѭӡng cong Success rate ÿiQKPӭFÿӝ phù hӧp và ÿѭӡng cong Prediction rate ÿiQKJLi ÿӝ chính xáFFiFSKѭѫQJSKiS)56,:R(Yj/5*LiWUӏ cӫa phҫn diӋn tích ErQGѭӟLFiFÿѭӡng cong (Areas Under Curves - $8& ÿѭӧc sӱ dөQJQKѭPӝt thông sӕ ÿӏQKOѭӧQJÿӇ kiӇm chӭQJFiFSKѭѫQJSKiS.Ӄt quҧ cho thҩ\FiFSKѭѫQJSKiSQj\Fy mӭFÿӝ phù hӧSYjÿӝ chính xác cao (AUC = 0,8~0,9). Thuұt toán mô hình trung bình Bayesian (BMA) cӫa phҫn mӅm thӕQJNr5ÿѭӧc áp dөQJÿӇ [iFÿӏnh các yӃu tӕ ҧQKKѭӣng nhҩt và các mô hình tӕLѭXWә hӧp yӃu tӕ ҧnh KѭӣQJÿӕi vӟLWUѭӧt lӣ. KӃt quҧ: x Tám yӃu tӕ ҧQKKѭӣQJOLrQTXDQÿӃQWUѭӧt lӣ: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡng giao WK{QJOѭӧQJPѭDOӟn nhҩWQăPJyFGӕc, thҥch hӑc, khoҧng cách ÿӃQÿӭt gãy, khoҧng FiFKÿӃn sông suӕLYjKѭӟng dӕc. x Bӕn yӃu tӕ ҧQKKѭӣng quan trӑng nhҩt: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡng giao thông, OѭӧQJPѭDOӟn nhҩt QăPYjJyFGӕc.

x 1ăP mô hình tӕLѭXWә hӧp yӃu tӕ ҧQKKѭӣng: Mô hình 1: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP và góc dӕc. iii Mô hình 2: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP góc dӕc, thҥch hӑc. Mô hình 3: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP góc dӕc, thҥch hӑc, khoҧQJFiFKÿӃQÿӭt gãy. Mô hình 4: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP góc dӕc, khoҧQJFiFKÿӃQÿӭt gãy.

Mô hình 5: cDRÿӝ, khoҧng cách ÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP góc dӕc, khoҧQJFiFKÿӃn sông suӕi. Trong 5 mô hình tӕLѭX, ÿiQKJLiWKHRhiӋu quҧ dӵ báo thì mô hình 1 là mô hình phù hӧp nhҩt vì sӱ dөng ít yӃu tӕ ҧQKKѭӣQJÿӇ dӵ báo (4 yӃu tӕ), ÿiQKJLiWKHRÿӝ chính xác dӵ báo thì mô hình 3 là mô hình phù hӧp tӕt nhҩt. PKѭѫQJSKiS:R(OjSKѭѫQJSKiSGӵ báo tӕt nhҩt YuFyÿӝ chính xác cao nhҩt, tiӃSÿӃn OjFiFSKѭѫQJSKiS)56,Yj/5 3KѭѫQJSKiS)5Yj6,WX\Fyÿӝ chính xác thҩSKѫQ QKѭQJÿѫQJLҧn trong tính toán nên cҫQ[HP[pWÿӇ áp dөng. iv ABSTRACT This study presents the results of studies related to landslide by rain and methods of landslide hazard maps.

The theories of two-dimensional permeability and the extended Mohr-Coulomb failure criterion for unsaturated soil were used to study the effects of rainwater on slope stability. Rainfall is supposed as a main trigger caused failure of the potential sliding slopes. Rainwater into the slope due to infiltration caused an increase in moisture content and negative pore water pressure; a decrease in matric suction and in shear strength on the failure surface. Therefore, slopes are reduced stability and can be failed.

In addition, the initial water table location and slope geometry also contribute significantly. A numerical model of analysis coupled seepage-stability used to simulate the seepage and slope stability under conditions of specific environment such as soil permeability, rainfall intensity, water table location and slope geometry in the study area. Finite element method used in seepage analysis, results of change of negative pore water pressure then used in the slope stability analysis to calculate the safety factor by the application of the limit equilibrium Morgenstern and Price slope stability method. The relationships between safety factor and rainfall intensity, soil permeability, angle slope, high slope were identified.

Result in analysis suggesting coefficient of permeability have an important role for the stability of the slope. When the rainy period is more than 3 days, the factor of safety will change much if the slope formed by soil having small coefficient of permeability and little change when the slope formed by soil having large coefficient of permeability. The factor of safety decreases more as the greater rainfall intensity. Threshold of rainfall intensity cause the failure of the slopes depending on the rainy period.

At the rainfall intensity of 10 mm/h, after a period of time more than five days, the slope will be destabilized. Applying statistical probability approaches, including frequency ratio (FR), statistical index (SI), weights of evidence (WoE) and logistic regression (LR) methods integrated with GIS analytical tools to produce landslide hazard maps in Khanh Vinh district, Khanh Hoa province. v A landslide inventory map identifies the definite and probable areas of existing landslides (231 landslides), and is the most basic requirement for a landslide hazard assessment. The product of a landslide inventory map is a spatial distribution of landslides as points or to scale.

Landslides-related factors chosen primarily upon available data and experiences of experts to the study area. In this study, there are eleven landslide-related factors were chosen: elevation, slope angle, slope direction, topographical wetness index, slope shape, lithology, distance from road, distance from drainage, distance from fault, normalized difference vegetation index and maximum precipitation in year. The weight maps of the influence factors were established based on the spatial relationship between the influencing factors and the distribution of existing landslides by FR, SI, WoE and LR methods. Landslide hazard maps were established upon the combination of the weight maps of the influence factors.

The landslide hazard index before dividing the landslide hazard zones should be standardized for histograms having normal distribution or near normal distribution. Standard Deviation method is used to divide the landslide hazard map into different landslide hazard zones. In this study, there are 5 levels of landslide hazard zone: very low, low, moderate, high and very high. Using the success rate curve and prediction rate curve assess the fit and accuracy of FR, SI, WoE and LR approaches.

The value of the area under these rate curves (AUC) was used as a quantitative parameter to validate the method. The results show that these approaches has the goodness of fit and the high accuracy (AUC = 0. Bayesian Model Average (BMA) of the R statistical software was applied to identify the most influential factors and the combinatorial optimization models of landslide-related factors. x There are eight landslide-related factors: elevation, distance from road, maximum precipitation in year, slope angle, lithology, distance from fault, distance from drainage and slope direction.

x There are four the most important landslide-related factors: elevation, distance from road, maximum precipitation in year and slope angle. vi x There are five combinatorial optimization models of landslide-related factors: Model 1: elevation, distance from road, maximum precipitation in year and slope angle. Model 2: elevation, distance from road, maximum precipitation in year, slope angle and lithology. Model 3: elevation, distance from road, maximum precipitation in year, slope angle, lithology and distance from fault.

Model 4: elevation, distance from road, maximum precipitation in year, slope angle and distance from fault. Model 5: elevation, distance from road, maximum precipitation in year, slope angle and distance from drainage. In five the optimal models, evaluated according to the level of forecasting efficiency, model 1 is the most optimization model due to using the least landslide-related factor (four factors); evaluated according to the level of forecasting accuracy, model 3 is the best optimization model. WoE method is the best forecasting method for highest accuracy, followed by FR, SI and LR.

FR and SI methods have slightly lower accuracy but the calculation is simple. Therefore, FR and SI methods should be considered for application in landslide hazard prediction. vii /Ӡ,&È0Ѫ1 Tác giҧ xin bày tӓ lòng biӃWѫQVkXVҳFÿӃn thҫ\3*676ĈұX9ăQ1Jӑ, thҫy TS. Tҥ QuӕF'NJQJÿmTXDQWkPJL~Sÿӥ, tұQWuQKKѭӟng dүn và cung cҩp tài liӋu tham khҧo giúp tác giҧ hoàn thành luұn án tiӃQVƭ Tác giҧ xin trân trӑng cҧPѫQTXêWKҫy cô giҧng viên Bӝ P{QĈӏa kӻ thuұW.KRDĈӏa chҩt và DҫXNKtÿmQKLӋt tình giҧng dҥ\YjJL~Sÿӥ tác giҧ trong thӡi gian làm luұn án tiӃQVƭ Chân thành cҧPѫQ7UѭӡQJĈҥi hӑF%iFKNKRD7S+&03KzQJĈjRWҥRVDXÿҥi hӑc, Khoa Kӻ thuұWĈӏa chҩt và Dҫu khí, Bӝ môQĈӏa kӻ thuұt, Phòng thí nghiӋPĈӏa kӻ thuұWÿmWҥo mӑLÿLӅu kiӋn thuұn lӧi cho tác giҧ hoàn thành nhiӋm vө.7 1JKLrQFӭXҧQKKѭӣQJFӫDPѭDÿӕLYӟLWUѭӧWOӣWUrQWKӃJLӟL.

7 1JKLrQFӭXҧQKKѭӣQJFӫDPѭDÿӕLYӟLWUѭӧWOӣӣ9LӋW1DP. 8 1JKLrQFӭXGӵEiRWUѭӧWOӣWUrQWKӃJLӟL. 10 1JKLrQFӭXGӵEiRWUѭӧWOӣӣ9LӋW1DP. 12 1KұQ[pWQJKLrQFӭXGӵEiRWUѭӧWOӣWUrQWKӃJLӟLYjӣ9LӋW1DP .16 9ӏWUtYQJQJKLrQFӭX .2 +ӋWҫQJĈqR%ҧR/ӝF -3ÿEO .3 +ӋWҫQJ1KD7Uang (Knt) .4 +ӋWҫQJĈDN5LXP .6 7UҫPWtFKV{QJKLӋQÿҥL D423) .9 /NJWtFK- VѭӡQWtFK SG4 .10 7UҫPWtFKV{QJ- ONJWtFK DS4.

22 ĈLӅXNLӋQÿӏDFKҩt công trình .1 3KӭFKӋFiW- FXӝLVӓLQJXӗQJӕFWUҫPWtFKV{QJ- ĈӋWӭ .2 3KӭFKӋÿҩWORҥLVpWFKӭDGăPVҥQÿDQJXӗQJӕF- ĈӋWӭ .3 3KӭFKӋWUҫPWtFKOөFQJX\rQ-XUDJLӳDYj&UHWDWUrQ .4 3KӭFKӋSKXQWUjR-XUDWUrQYj&UHWD .5 3KӭFKӋ[kPQKұS&UHWD- Paleogen. 23 ĈLӅXNLӋQÿӏDFKҩWWKӫ\YăQ .1 7ҫQJFKӭDQѭӟFOәKәQJ- WUҫPWtFKĈӋ7ӭ .2 7ҫQJFKӭDQѭӟFNKHQӭW- WUҫPWtFKSKXQWUjR .3 7ҫQJFKӭDQѭӟF NKHQӭW- WUҫPWtFKOөFQJX\rQ .24 &iFÿӭWJm\NLӃQWҥR.1 ĈӏDKuQKEyFPzQ .2 &iFEӅPһWÿӗQJEҵQJEyFPzQ- UӱDWU{LWtFKWөYHQV{QJ .28 ĈһFÿLӇPYӓSKRQJKya .1 9ӓSKRQJKyDSKiWWULӇQWUrQFiFÿiWUҫPWtFKVpWNӃWEӝWNӃWYjFiWNӃW .2 9ӓSKRQJKyDSKiWWULӇQWUrQFiF ÿiJUDQLWYjU\ROLW. 30 +LӋQWUҥQJWUѭӧWOӣÿҩWÿi .1 3KkQORҥLWUѭӧW.3 1JX\rQQKkQWUѭӧW .38 &+ѬѪ1* 1*+,Ç1&Ӭ8Ҧ1++ѬӢ1*&Ӫ$0Ѭ$ĈӔ,9Ӟ,75ѬӦ7/Ӣ 39 &ѫFKӃFӫDTXiWUuQKPѭDJk\PҩWәQÿӏQKPiLGӕF .39 ĈһFÿLӇPYӅWKҩPYjVӭFFKӕQJFҳWWURQJÿҩWNK{QJEmRKzD .KiLQLӋPÿҩWNK{QJEmRKzD.2 ĈѭӡQJFRQJÿһFWUѭQJÿҩW-QѭӟF 6:&& .3 +jPWKҩPWKӫ\OӵF .4 7KҩPNK{QJәQÿӏQKWURQJÿҩWNK{QJEmRKzD .5 6ӭFFKӕQJFҳWWURQJÿҩWNK{QJEmRKzD .47 7tQKWRiQәQÿӏQKPiLGӕFGѭӟLҧQKKѭӣQJFӫDPѭD .49 &iFWK{QJVӕQJKLrQFӭX .2 3KkQWtFKWKҩP .3 3KkQWtFKәQÿӏQK .ӃWTXҧSKkQWtFK .64 &ѫVӣOêWKX\ӃWFiFSKѭѫQJSKiS[iFVXҩWWKӕQJNr .1 3KѭѫQJSKiSWӹVӕWҫQVXҩW )5- Frequency Ratio) .2 3KѭѫQJSKiSFKӍVӕWKӕQJNr 6,- Statistic Index) .3 3KѭѫQJSKiSWUӑQJVӕFKӭQJFӭ :R(- Weights of Evidence) .4 3KѭѫQJSKiSKӗLTX\ORJLVWLF /5- Logistic Regression). 68 1JX\rQOêWKjQKOұSEҧQÿӗWDLELӃQWUѭӧWOӣ .69 +ӋWKӕQJWK{QJWLQÿӏDOê *,6 WURQJQJKLrQFӭXWUѭӧWOӣ .1 %ҧQÿӗSKkQEӕÿLӇPWUѭӧWOӣ .2 &iF\ӃXWӕҧQKKѭӣQJ .3 /ӵDFKӑQFiF\ӃXWӕҧQKKѭӣQJ .4 0{KuQKVӕÿӝFDR 'LJLWDO(OHYDWLRQ0RGHO- DEM) .8 &KӍVӕҭPѭӟWÿӏDKuQK 7:,- Topographic Wetness Index) .9 /ѭӧQJPѭDOӟQQKҩWQăP 03- Maximum Precipitation) .11 &KӍVӕWKӵFYұW 1'9,- Normalized Difference Vegetation Index) .KRҧQJFiFKÿӃQV{QJVXӕL ')'- Distance From Drainage) .KRҧQJFiFKÿӃQÿѭӡQJJLDRWK{QJ ')5- Distance From Road) .KRҧQJFiFKÿӃQÿӭWJm\ '))- Distance From Fault).

86 4X\WUuQKWKjQKOұSEҧQÿӗWDLELӃQWUѭӧWOӣ. 88 ӬQJGөQJSKѭѫQJSKiS[iFVXҩWWKӕQJNrWKjQKOұSEҧQÿӗWDLELӃQWUѭӧWOӣ .1 7UuQKWӵWtQKWRiQWUӑQJVӕWKHRFiFSKѭѫQJSKiS .ӃWTXҧWtQKWRiQ .3 %ҧQÿӗWUӑQJVӕFiF\ӃXWӕҧQKKѭӣQJ .LӇPWUDWtQKÿӝFOұSJLӳDFiF\ӃXWӕҧQKKѭӣQJ .5 %ҧQÿӗWDLELӃQWUѭӧWOӣ .LӇPFKӭQJYjVRViQKFiFSKѭѫQJSKiS .7 ĈiQKJLiFiF\ӃXWӕҧQKKѭӣQJYjOӵDFKӑQP{KuQKWӕLѭX .132 xii DANH MӨC HÌNH ҦNH Hình 2.1 Vӏ trí vùng nghiên cӭu .16 +uQK6ѫÿӗ ÿӏa chҩt huyӋQ.3 Mӝt sӕ dҥQJWUѭӧt phә biӃn theo USGS (2004) .32 +uQK6ѫÿӗ các vӏ WUtWUѭӧt lӣ trên các tuyӃn giao thông huyӋQ.5 Hình ҧnh các vӏ WUtWUѭӧt lӣ 9696WUrQÿRҥn Khánh Lê - Hòn Giao, tuyӃQÿѭӡng Nha Trang - Ĉj/ҥt .